Real-time damage identification in stiffened plates: A two-stage Kalman filter approach with U-V transformations

Ensuring the structural integrity of stiffened plates is crucial for aircraft safety, as these components serve as primary load-bearing elements. Traditional damage identification methods based on the Kalman filter face challenges in high-dimensional systems due to excessive computational complexity, limiting their real-time applicability. To address this issue, this study proposes a novel two-stage Kalman filtering approach based on U-V transformations, specifically designed to enhance damage identification efficiency in stiffened plates. The U-V transformation effectively decouples state variables from unknown parameters, significantly reducing matrix dimensionality and computational load. Simulation results demonstrate that the traditional unscented Kalman filter (UKF) incurs a computational delay exceeding the sampling interval, leading to time lags and degraded accuracy in damage estimation. In contrast, the proposed U-V based two-stage Kalman filter reduces damage identification time to just 3% of that required by the UKF while maintaining high accuracy. Furthermore, the computational time per iteration is significantly lower than the sampling interval, demonstrating the method’s potential for real-time structural health monitoring.